Apolipoprotein-B (apoB) containing lipoproteins, as marked by plasma low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG), are causal, heritable risk factors for myocardial infarction (MI), the leading cause of death worldwide. New strategies to lower apoB- containing lipoproteins and MI risk are needed and studying inherited human genetic variation can lead to therapeutic targets that reduce MI risk. Through population-based sequencing and genotyping studies we and others are deriving systematic catalogues of all protein-coding DNA variants present in tens of thousands of individuals characterized for plasma LDL-C, TG, and MI status. However, most protein-coding variants discovered through these approaches are neutral, i.e., they have little or no effect on the function of the protein encoded by the gene. As such, attempts to associate protein-coding variants with LDL-C, TG, or MI face a tremendous signal to noise problem where the signal from functional alleles is overwhelmed by the noise from neutral alleles. Therefore, the critical barrie facing human genetic studies is distinguishing alleles causal for disease from nonpathogenic variants. To overcome this challenge, we propose a systematic cell-based functional genomics approach that: 1) establishes cell-based assays to measure apoB biology; 2) tests the effect of specific genes and variants on cellular assays; and 3) analyzes association with either lipids or MI risk after weighting alleles based on their functional significance in these cellular assays. We hypothesize that the combination of genetics with systematically- acquired functional data in cells can pinpoint new genes responsible for not only altered LDL- C or TG but also MI risk. To test this hypothesis, we propose the following aims: Aim 1 - To robustly establish systematic overexpression, knockdown and complementation for testing multiple genes and variants in parallel for apoB-relevant functions in cells; Aim 2 - To scale up the application of our technology to test 120 genes with the goal of identifying additional novel LDL-C and TG genes; and Aim 3 - To exploit our technology to decipher which LDL-C and TG genes also confer risk for MI. This proposal addresses a fundamental challenge to modern genetics (to distinguish functionally-relevant from neutral variants in an individual's genome) by applying novel technology (systematic cell-based functional characterization of genes and genetic variants) to a significant unmet health need (improved treatments for MI).